Blood-borne pathogens are a significant healthcare problem. A delayed or improper diagnosis of a bacterial or fungal infection can result in sepsis, a serious, and often deadly, inflammatory response to the infection. Sepsis is the 10th leading cause of death in the United States. Early detection of bacterial infections in blood is the key to preventing the onset of sepsis. Traditional methods of detection and identification of blood-borne infection include blood culture and antibiotic susceptibility assays. Those methods typically require culturing cells, which can be expensive and can take as long as 72 hours. Often, septic shock will occur before cell culture results can be obtained.
Alternative methods for detection of pathogens, particularly bacteria and fungi, have been described by others. Those methods include molecular detection methods, antigen detection methods, and metabolite detection methods. Molecular detection methods, whether involving hybrid capture or polymerase chain reaction (PCR), require high concentrations of purified DNA for detection. Both antigen detection and metabolite detection methods also require a relatively large amount of pathogens and have high limit of detection (usually >104 CFU/mL), thus requiring an enrichment step prior to detection. This incubation/enrichment period is intended to allow for the growth of bacteria or fungi and an increase in cell numbers to more readily aid in identification. In many cases, a series of two or three separate incubations is needed to isolate the target bacteria or fungus. However, such enrichment steps require a significant amount of time (e.g., at least a few days to a week) and can potentially compromise test sensitivity by killing some of the cells sought to be measured.
There is a need for methods for isolating target analytes, such as bacteria, from a sample, such as a blood sample, without an additional enrichment step. There is also a need for methods of isolating target analytes that are fast and sensitive in order to provide data for patient treatment decisions in a clinically relevant time frame.